In computer vision applications intended for image recognition, it is often required to differentiate between visually similar image classes. For instance, many images from the same group of interest may have the same graphics and almost identical caption apart from small changes within the image. One solution to such a problem is first to identify visually similar locations in all similar classes. Then, relative to those locations, to attempt to find dissimilar discriminative locations.
Image classification includes a broad range of decision-theoretic approaches to the identification of images or parts thereof. Classification algorithms are generally based on the assumption that the image depicts one or more features, such as geometric parts in the case of a manufacturing classification system, or spectral regions in the case of remote sensing, or the like, and that each of these features belongs to one of several distinct and exclusive classes. The classes may be specified a priori by an analyst, as in supervised classification or automatically clustered into sets of prototype classes, as in unsupervised classification, where the analyst merely specifies the number of desired categories.
Image classification analyzes numerical properties of various image features and organizes data into categories. Supervised classification algorithms typically employ two phases of processing: training and predicting. In the initial training phase, characteristic properties of typical image features are isolated from a plurality of images that correspond to the class and, based on these, a unique description of each classification category, i.e. training class, is created. In the subsequent predicting phase, these feature-space partitions are used to classify image features. Unsupervised classification algorithms typically do not utilize a training set but rather are configured to automatically discover structure in data provided thereto in order to generalize mapping from inputs to outputs. In order that such generalization be accurate, a plurality of representative images from each class need to be processed.